Fashion Forward: Forecasting Visual Style in Fashion
arXiv:1705.06394
Abstract
What is the future of fashion? Tackling this question from a data-driven vision perspective, we propose to forecast visual style trends before they occur. We introduce the first approach to predict the future popularity of styles discovered from fashion images in an unsupervised manner. Using these styles as a basis, we train a forecasting model to represent their trends over time. The resulting model can hypothesize new mixtures of styles that will become popular in the future, discover style dynamics (trendy vs. classic), and name the key visual attributes that will dominate tomorrow's fashion. We demonstrate our idea applied to three datasets encapsulating 80,000 fashion products sold across six years on Amazon. Results indicate that fashion forecasting benefits greatly from visual analysis, much more than textual or meta-data cues surrounding products.
ICCV 2017. Project page: https://cvhci.anthropomatik.kit.edu/~zalhalah/prj_fashion_forecast.html
References in corpus (1)
Cited by in corpus (14)
- Fashion Meets Computer Vision: A Survey
- Visually-Aware Fashion Recommendation and Design with Generative Image Models
- Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends
- Learning Type-Aware Embeddings for Fashion Compatibility
- VITON: An Image-based Virtual Try-on Network
- Coherent and Controllable Outfit Generation
- Context-Aware Visual Compatibility Prediction
- Creating Capsule Wardrobes from Fashion Images
- Complete the Look: Scene-based Complementary Product Recommendation
- Deep Imbalanced Attribute Classification using Visual Attention Aggregation
- Brand > Logo: Visual Analysis of Fashion Brands
- Image-based Virtual Fitting Room
- Fashion is Taking Shape: Understanding Clothing Preference Based on Body Shape From Online Sources
- Compare and Contrast: Learning Prominent Visual Differences